def get_record_keeper(csv_folder,
tensorboard_folder=None,
global_db_path=None,
experiment_name=None,
is_new_experiment=True,
save_figures=False,
save_lists=False):
try:
import record_keeper as record_keeper_package
from torch.utils.tensorboard import SummaryWriter
record_writer = record_keeper_package.RecordWriter(
folder=csv_folder,
global_db_path=global_db_path,
experiment_name=experiment_name,
is_new_experiment=is_new_experiment,
save_lists=save_lists)
tensorboard_writer = SummaryWriter(
log_dir=tensorboard_folder
) if tensorboard_folder is not None else None
record_keeper = record_keeper_package.RecordKeeper(
tensorboard_writer=tensorboard_writer,
record_writer=record_writer,
attributes_to_search_for=c_f.
list_of_recordable_attributes_list_names(),
save_figures=save_figures)
return record_keeper, record_writer, tensorboard_writer
except ModuleNotFoundError as e:
logging.warn(e)
logging.warn("There won't be any logging or model saving.")
logging.warn("To fix this, pip install record-keeper tensorboard")
return None, None, None
def get_record_keeper():
# record_keeper is a useful package for logging data during training and testing
# You can use the trainers and testers without record_keeper.
# But if you'd like to install it, then do pip install record_keeper
# See more info about it here https://github.com/KevinMusgrave/record_keeper
try:
import os
import errno
import record_keeper as record_keeper_package
from torch.utils.tensorboard import SummaryWriter
def makedir_if_not_there(dir_name):
try:
os.makedirs(dir_name)
except OSError as e:
if e.errno != errno.EEXIST:
raise
pkl_folder = "example_logs"
tensorboard_folder = "example_tensorboard"
makedir_if_not_there(pkl_folder)
makedir_if_not_there(tensorboard_folder)
pickler_and_csver = record_keeper_package.PicklerAndCSVer(pkl_folder)
tensorboard_writer = SummaryWriter(log_dir=tensorboard_folder)
return record_keeper_package.RecordKeeper(
tensorboard_writer, pickler_and_csver,
["record_these", "learnable_param_names"])
except ModuleNotFoundError:
return None
def get_record_keeper(csv_folder, tensorboard_folder, global_db_path=None, experiment_name=None, is_new_experiment=True):
try:
import record_keeper as record_keeper_package
from torch.utils.tensorboard import SummaryWriter
record_writer = record_keeper_package.RecordWriter(csv_folder, global_db_path, experiment_name, is_new_experiment)
tensorboard_writer = SummaryWriter(log_dir=tensorboard_folder)
record_keeper = record_keeper_package.RecordKeeper(tensorboard_writer, record_writer, ["record_these", "learnable_param_names"])
return record_keeper, record_writer, tensorboard_writer
except ModuleNotFoundError as e:
logging.warn(e)
return None, None, None
def get_record_keeper(pkl_folder, tensorboard_folder):
try:
import record_keeper as record_keeper_package
from torch.utils.tensorboard import SummaryWriter
pickler_and_csver = record_keeper_package.PicklerAndCSVer(pkl_folder)
tensorboard_writer = SummaryWriter(log_dir=tensorboard_folder)
record_keeper = record_keeper_package.RecordKeeper(
tensorboard_writer, pickler_and_csver,
["record_these", "learnable_param_names"])
return record_keeper, pickler_and_csver, tensorboard_writer
except ModuleNotFoundError as e:
logging.warn(e)
return None, None, None
def get_record_keeper(csv_folder,
tensorboard_folder,
global_db_path=None,
experiment_name=None,
is_new_experiment=True,
save_figures=False):
try:
import record_keeper as record_keeper_package
from torch.utils.tensorboard import SummaryWriter
record_writer = record_keeper_package.RecordWriter(
csv_folder, global_db_path, experiment_name, is_new_experiment)
tensorboard_writer = SummaryWriter(log_dir=tensorboard_folder)
record_keeper = record_keeper_package.RecordKeeper(
tensorboard_writer,
record_writer, ["record_these", "learnable_param_names"],
save_figures=save_figures)
return record_keeper, record_writer, tensorboard_writer
except ModuleNotFoundError as e:
logging.warn(e)
logging.warn("There won't be any logging or model saving.")
logging.warn("To fix this, pip install record-keeper tensorboard")
return None, None, None
def makedir_if_not_there(dir_name):
try:
os.makedirs(dir_name)
except OSError as e:
if e.errno != errno.EEXIST:
raise
pkl_folder = "example_logs"
tensorboard_folder = "example_tensorboard"
makedir_if_not_there(pkl_folder)
makedir_if_not_there(tensorboard_folder)
pickler_and_csver = record_keeper_package.PicklerAndCSVer(pkl_folder)
tensorboard_writer = SummaryWriter(log_dir=tensorboard_folder)
record_keeper = record_keeper_package.RecordKeeper(
tensorboard_writer, pickler_and_csver,
["record_these", "learnable_param_names"])
except ModuleNotFoundError:
record_keeper = None
##############################
########## Training ##########
##############################
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Set trunk model and replace the softmax layer with an identity function
trunk = models.resnet18(pretrained=True)
trunk_output_size = trunk.fc.in_features
trunk.fc = Identity()
